Set up
suppressPackageStartupMessages({
library(tidyverse)
})
Read in data and process
# Vector to order timepoints
timepoints <- c("Diagnosis", "Progressive", "Recurrence", "xDeceased", "Second Malignancy", "Unavailable")
pbta_df <- readr::read_tsv(pbta_file, guess_max = 100000, show_col_types = FALSE) %>%
select(Kids_First_Participant_ID, Kids_First_Biospecimen_ID, cg_multiple, cg_id, cgGFAC, tumor_descriptor)
tmb_vaf_df <- readr::read_tsv(tmb_vaf_file, guess_max = 100000, show_col_types = FALSE) %>%
filter(!tmb >= 10) %>%
select(Kids_First_Biospecimen_ID, Variant_Classification, gene_protein, mutation_count, region_size, tmb, VAF)
genomic_paired_df <- readr::read_tsv(genomic_paired_file, guess_max = 100000, show_col_types = FALSE) %>%
left_join(pbta_df, by = c("Kids_First_Participant_ID")) %>%
left_join(tmb_vaf_df, by = c("Kids_First_Biospecimen_ID")) %>%
filter(!is.na(tmb))
no_samples_with_tmb <- print(length(unique(genomic_paired_df$Kids_First_Participant_ID)))
[1] 116
# Attention as some bs specimen might not have TMB.
# If that happens, we will end up with samples lacking timepoints.
# Let's identify these samples and remove them for now.
df <- genomic_paired_df %>%
select(Kids_First_Participant_ID, tumor_descriptor) %>%
unique() %>%
arrange(Kids_First_Participant_ID, tumor_descriptor) %>%
group_by(Kids_First_Participant_ID) %>%
summarize(tumor_descriptor_sum = str_c(tumor_descriptor, collapse = ";")) %>%
filter(!tumor_descriptor_sum %in% c("Diagnosis", "Progressive", "Recurrence", "Second Malignancy", "Unavailable", "Deceased", "Progressive;Progressive")) %>%
left_join(genomic_paired_df, by = c("Kids_First_Participant_ID")) %>%
filter(!cg_id == "NA") %>%
mutate(tumor_descriptor = case_when(grepl("Deceased", tumor_descriptor) ~ "xDeceased",
TRUE ~ tumor_descriptor),
match_id = paste(tumor_descriptor, Kids_First_Participant_ID, sep = "_"),
tumor_descriptor = factor(tumor_descriptor),
tumor_descriptor = fct_relevel(tumor_descriptor, timepoints))
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `tumor_descriptor = fct_relevel(tumor_descriptor, timepoints)`.
Caused by warning:
! 1 unknown level in `f`: Unavailable
no_samples <- print(length(unique(df$Kids_First_Participant_ID)))
[1] 107
# Let's count number of samples
count_df <- df %>%
group_by(tumor_descriptor, cg_id, match_id, Variant_Classification) %>%
dplyr::count(cg_id)
Define parameters for plots
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE)
# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$color_names
Alterations per timepoint
# Define parameters for function
x_value <- count_df$tumor_descriptor
title <- paste("Variant types in PBTA cohort", sep = " ")
# Run function
fname <- paste0(plots_dir, "/", "Alteration_type_timepoints_barplots.pdf")
print(fname)
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/Alteration_type_timepoints_barplots.pdf"
p <- create_stacked_barplot_variant(count_df = count_df, x = x_value, palette = palette, title = title)
pdf(file = fname, width = 6, height = 6)
print(p)
dev.off()
quartz_off_screen
2

Alterations per timepoint in each cancer type

Alterations per timepoint in each cancer type and kids_id
cg_id_id <- as.character(unique(count_df$cg_id))
cg_id_id <- sort(cg_id_id, decreasing = FALSE)
cg_id_id
[1] "Adamantinomatous Craniopharyngioma" "Atypical Teratoid Rhabdoid Tumor" "Chordoma"
[4] "Choroid plexus carcinoma" "CNS Embryonal tumor" "Craniopharyngioma"
[7] "Diffuse midline glioma" "Dysembryoplastic neuroepithelial tumor" "Embryonal tumor with multilayer rosettes"
[10] "Ependymoma" "Ewing sarcoma" "Ganglioglioma"
[13] "Glial-neuronal tumor" "Hemangioblastoma" "High-grade glioma"
[16] "Low-grade glioma" "Malignant peripheral nerve sheath tumor" "Medulloblastoma"
[19] "Meningioma" "Neuroblastoma" "Neurofibroma/Plexiform"
[22] "Pilocytic astrocytoma" "Schwannoma"
# Define parameters for function
x_value <- count_df$match_id
title <- paste("Variant types in PBTA cohort across cancer groups", sep = " ")
# Run function
fname <- paste0(plots_dir, "/", "Alteration_type_timepoints_cg_id_kids_barplots.pdf")
print(fname)
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/Alteration_type_timepoints_cg_id_kids_barplots.pdf"
p <- create_stacked_barplot_variant_cg_id(count_df = count_df, x = x_value, palette = palette, title = title)
pdf(file = fname, width = 30, height = 30)
print(p)
dev.off()
quartz_off_screen
2

Alterations per timepoint in each cancer type and timepoint
model
tm_df_plot <- df %>%
filter(!is.na(timepoints_models)) %>%
group_by(tumor_descriptor, cg_id, timepoints_models, match_id, Variant_Classification) %>%
dplyr::count(cg_id)
tm <- as.character(unique(tm_df_plot$timepoints_models))
tm <- sort(tm, decreasing = FALSE)
tm
[1] "Dx-Dec" "Dx-Pro" "Dx-Pro-Dec" "Dx-Pro-Rec" "Dx-Pro-Rec-Dec" "Dx-Rec" "Dx-Rec-Dec" "Dx-SM"
[9] "Pro-Dec" "Pro-Rec" "Pro-Rec-Dec" "Rec-Dec" "Rec-SM"
# Loop through variable
for (i in seq_along(tm)){
print(i)
df_sub <- tm_df_plot %>%
filter(timepoints_models == tm[i])
# Define parameters for function
x_value <- df_sub$match_id
title <- paste(tm[i])
if (i %in% c(1,2,6)){
rows <- 2
}else{
rows <- 1
}
# Run function
p <- create_stacked_barplot_variant_cg_id(count_df = df_sub, x = x_value, palette = palette, title = title, rows = rows)
}
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
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[1] 13













sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.5.2
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] flextable_0.9.2 ggthemes_4.2.4 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] sass_0.4.7 bit64_4.0.5 vroom_1.6.3 jsonlite_1.8.7 carData_3.0-5
[6] bslib_0.5.0 shiny_1.7.4.1 askpass_1.2.0 fontLiberation_0.1.0 yaml_2.3.7
[11] gdtools_0.3.3 pillar_1.9.0 backports_1.4.1 glue_1.6.2 uuid_1.1-0
[16] digest_0.6.33 promises_1.2.0.1 ggsignif_0.6.4 colorspace_2.1-0 htmltools_0.5.5
[21] httpuv_1.6.11 gfonts_0.2.0 fontBitstreamVera_0.1.1 pkgconfig_2.0.3 httpcode_0.3.0
[26] broom_1.0.5 xtable_1.8-4 scales_1.2.1 later_1.3.1 officer_0.6.2
[31] fontquiver_0.2.1 tzdb_0.4.0 openssl_2.1.0 timechange_0.2.0 generics_0.1.3
[36] farver_2.1.1 car_3.1-2 ellipsis_0.3.2 ggpubr_0.6.0 cachem_1.0.8
[41] withr_2.5.0 cli_3.6.1 magrittr_2.0.3 crayon_1.5.2 mime_0.12
[46] evaluate_0.21 fansi_1.0.4 xml2_1.3.5 rstatix_0.7.2 textshaping_0.3.6
[51] tools_4.2.3 data.table_1.14.8 hms_1.1.3 lifecycle_1.0.3 munsell_0.5.0
[56] zip_2.3.0 compiler_4.2.3 jquerylib_0.1.4 systemfonts_1.0.4 rlang_1.1.1
[61] rstudioapi_0.15.0 labeling_0.4.2 rmarkdown_2.23 gtable_0.3.3 abind_1.4-5
[66] curl_5.0.2 R6_2.5.1 knitr_1.43 fastmap_1.1.1 bit_4.0.5
[71] utf8_1.2.3 rprojroot_2.0.3 ragg_1.2.5 stringi_1.7.12 parallel_4.2.3
[76] crul_1.4.0 Rcpp_1.0.11 vctrs_0.6.3 tidyselect_1.2.0 xfun_0.39
---
title: "Classification of Variants across paired longitudinal samples in the PBTA Cohort"
author: 'Antonia Chroni <chronia@chop.edu> for D3B'
date: "2023"
output:
  html_notebook:
    toc: TRUE
    toc_float: TRUE
---

# Set up
```{r load-library}
suppressPackageStartupMessages({
  library(tidyverse)
})
```

# Directories and File Inputs/Outputs
```{r set-dir-and-file-names}
# Detect the ".git" folder -- this will be in the project root directory
# Use this as the root directory to ensure proper sourcing of functions 
# no matter where this is called from
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "tmb-vaf-longitudinal")
results_dir <- file.path(analysis_dir, "results")
input_dir <- file.path(analysis_dir, "input")
files_dir <- file.path(root_dir, "analyses", "sample-distribution-analysis", "results")

# Input files
pbta_file <- file.path(files_dir, "pbta.tsv") # file from add-sample-distribution module
genomic_paired_file <- file.path(files_dir, "genomic_assays_matched_time_points.tsv")
tmb_vaf_file <- file.path(results_dir, "tmb_vaf_genomic.tsv")
palette_file <- file.path(root_dir, "figures", "palettes", "oncoprint_color_palette.tsv")

# File path to plot directory
plots_dir <-
  file.path(analysis_dir, "plots")
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}

source(paste0(root_dir, "/figures/scripts/theme.R"))
source(paste0(analysis_dir, "/util/function-create-barplot.R"))
```

# Read in data and process

```{r load-process-inputs}
# Vector to order timepoints
timepoints <- c("Diagnosis", "Progressive", "Recurrence", "xDeceased", "Second Malignancy", "Unavailable")

pbta_df <- readr::read_tsv(pbta_file, guess_max = 100000, show_col_types = FALSE) %>% 
  select(Kids_First_Participant_ID, Kids_First_Biospecimen_ID, cg_multiple, cg_id, cgGFAC, tumor_descriptor)

tmb_vaf_df <- readr::read_tsv(tmb_vaf_file, guess_max = 100000, show_col_types = FALSE) %>% 
  filter(!tmb >= 10) %>% 
  select(Kids_First_Biospecimen_ID, Variant_Classification, gene_protein, mutation_count,	region_size, tmb, VAF)

genomic_paired_df <- readr::read_tsv(genomic_paired_file, guess_max = 100000, show_col_types = FALSE) %>%
  left_join(pbta_df, by = c("Kids_First_Participant_ID")) %>% 
  left_join(tmb_vaf_df, by = c("Kids_First_Biospecimen_ID")) %>%
  filter(!is.na(tmb))

no_samples_with_tmb <- print(length(unique(genomic_paired_df$Kids_First_Participant_ID)))

# Attention as some bs specimen might not have TMB.
# If that happens, we will end up with samples lacking timepoints.
# Let's identify these samples and remove them for now.

df <- genomic_paired_df %>%
  select(Kids_First_Participant_ID, tumor_descriptor) %>% 
  unique() %>%
  arrange(Kids_First_Participant_ID, tumor_descriptor) %>%
  group_by(Kids_First_Participant_ID) %>%
  summarize(tumor_descriptor_sum = str_c(tumor_descriptor, collapse = ";")) %>%
  filter(!tumor_descriptor_sum %in% c("Diagnosis", "Progressive", "Recurrence", "Second Malignancy", "Unavailable", "Deceased", "Progressive;Progressive")) %>%
  left_join(genomic_paired_df, by = c("Kids_First_Participant_ID")) %>%
  filter(!cg_id == "NA") %>% 
  mutate(tumor_descriptor = case_when(grepl("Deceased", tumor_descriptor) ~ "xDeceased",
                               TRUE ~ tumor_descriptor),
         match_id = paste(tumor_descriptor, Kids_First_Participant_ID, sep = "_"),
         tumor_descriptor = factor(tumor_descriptor),
         tumor_descriptor = fct_relevel(tumor_descriptor, timepoints))

no_samples <- print(length(unique(df$Kids_First_Participant_ID)))

# Let's count number of samples 
count_df <- df %>% 
  group_by(tumor_descriptor, cg_id, match_id, Variant_Classification) %>% 
  dplyr::count(cg_id) 

``` 

# Define parameters for plots

```{r define-parameters-for-plots}
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE) 

# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$color_names
```

# Alterations per timepoint

```{r plot-timepoint, fig.width = 6, fig.height = 6, fig.fullwidth = TRUE}
# Define parameters for function
x_value <- count_df$tumor_descriptor
title <- paste("Variant types in PBTA cohort", sep = " ")

# Run function
fname <- paste0(plots_dir, "/", "Alteration_type_timepoints_barplots.pdf")
print(fname)
p <- create_stacked_barplot_variant(count_df = count_df, x = x_value, palette = palette, title = title)
pdf(file = fname, width = 6, height = 6)
print(p)
dev.off()
```

# Alterations per timepoint in each cancer type

```{r plot-cg-id, fig.width = 25, fig.height = 30, fig.fullwidth = TRUE}
# Define parameters for function
x_value <- count_df$tumor_descriptor
title <- paste("Variant types in PBTA cohort across cancer groups", sep = " ")
rows <- 5

# Run function
fname <- paste0(plots_dir, "/", "Alteration_type_timepoints_cg_id_barplots.pdf")
print(fname)
p <- create_stacked_barplot_variant_cg_id(count_df = count_df, x = x_value, palette = palette, title = title, rows = rows)
pdf(file = fname, width = 25, height = 30)
print(p)
dev.off()
```


# Alterations per timepoint in each cancer type and kids_id

```{r plot-cg-id-kids, fig.width = 30, fig.height = 30, fig.fullwidth = TRUE}
cg_id_id <- as.character(unique(count_df$cg_id)) 
cg_id_id <- sort(cg_id_id, decreasing = FALSE)
cg_id_id

# Define parameters for function
x_value <- count_df$match_id
title <- paste("Variant types in PBTA cohort across cancer groups", sep = " ")
rows <- 5

# Run function
fname <- paste0(plots_dir, "/", "Alteration_type_timepoints_cg_id_kids_barplots.pdf")
print(fname)
p <- create_stacked_barplot_variant_cg_id(count_df = count_df, x = x_value, palette = palette, title = title, rows = rows)
pdf(file = fname, width = 30, height = 30)
print(p)
dev.off()
```

# Alterations per timepoint in each cancer type and timepoint model

```{r plot-timepoint-model, fig.width = 20, fig.height = 10, fig.fullwidth = TRUE}
tm_df_plot <- df %>%
  filter(!is.na(timepoints_models)) %>% 
  group_by(tumor_descriptor, cg_id, timepoints_models, match_id, Variant_Classification) %>% 
  dplyr::count(cg_id)

tm <- as.character(unique(tm_df_plot$timepoints_models))
tm <- sort(tm, decreasing = FALSE)
tm

# Loop through variable
for (i in seq_along(tm)){
  print(i)
  df_sub <- tm_df_plot %>%
      filter(timepoints_models == tm[i])
  
  # Define parameters for function
  x_value <- df_sub$match_id
  title <- paste(tm[i])
  
  if (i %in% c(1,2,6)){
    rows <- 2
    }else{
      rows <- 1
      }
  
  # Run function
  p <- create_stacked_barplot_variant_cg_id(count_df = df_sub, x = x_value, palette = palette, title = title, rows = rows)
  
}
```


```{r echo=TRUE}
sessionInfo()
```
